code: def get_array_with_gaps(gap_dict=None, gap_value: float = -100.0):
      """
      Function for generating synthetic data and gaps in it with predefined length
      and location
  
      :param gap_dict: a dictionary with omissions, where the key is the index in
      the time series from which the gap will begin. The key value is the length
      of the gap (elements). -1 in the value means that a skip is generated until
      the end of the array
      :param gap_value: value indicating a gap in the array
  
      :return array_with_gaps: an array with gaps
      :return real_values: an array with actual values in gaps
      """
  
      real_values = generate_synthetic_data(length=100)
  
      if gap_dict is None:
          gap_dict = {20: 5, 70: 10}
      array_with_gaps = generate_gaps_in_ts(
          array_without_gaps=real_values,
          gap_dict=gap_dict,
          gap_value=gap_value,
      )
  
      return array_with_gaps, real_values
  
file_path: FEDOT/examples/simple/time_series_forecasting/gapfilling.py
imports: import matplotlib.pyplot
  import numpy
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.node import PipelineNode
  from fedot.utilities.synth_dataset_generator import generate_synthetic_data
  from fedot.utilities.ts_gapfilling import ModelGapFiller, SimpleGapFiller
label_desc: Use image node_id 2 for calling a global function with example usage: get_array_with_gaps(gap_dict, gap_value) and returns: array_with_gaps,  real_values